February 2024 arXiv papers — page 46

D. Bartoli, A. Giannoni, G. Marino

Over the past few decades, there has been extensive research on scattered subspaces, partly because of their link to MRD codes. These subspaces can be characterized using linearized polynomials over finite fields. Within this context, scattered sequences extend the concept of scattered polynomials and can be viewed as geometric equivalents of exceptional MRD

Arijeet Ganguli, Marco Ruffini

We present a multi-tenant multi-wavelength upstream transmission scheme for virtualised PONs, enabling compliance with latency-oriented Service Level Agreements (SLAs). Our analysis highlights an important trade-off between single-channel vs. multi-channel PONs, depending on ONUs tuning time.

Blanca Climent-Ezquerra, Mario Durán, Elva Ortega-Torres, Marko Rojas-Medar

We study the existence of reproductive weak solutions for a system of equations describing a solidification process of a binary alloy confined into a bounded and regular domain in $\mathbb{R}^3$, having mixed boundary conditions.

Lu Ye, Ze Tao, Yong Huang, Yang Li

Self-attention is an essential component of large language models (LLM) but a significant source of inference latency for long sequences. In multi-tenant LLM serving scenarios, the compute and memory operation cost of self-attention can be optimized by using the probability that multiple LLM requests have shared system prompts in prefixes. In this paper, we

Lan Bo, Rongzhi Zhao, Xichao Zhang, Masahito Mochizuki

Magnetic skyrmion bags are composite topological spin textures with arbitrary topological charges. Here, we computationally study the transient rotational motion of skyrmion bags, which is characterized by a global rotation of the inner skyrmions around the central point. Distinct from conventional rotational modes found in skyrmions, the observed rotation i

Yu Tian, Xiao Yang, Yinpeng Dong, Heming Yang

Extremely large image generators offer significant transformative potential across diverse sectors. It allows users to design specific prompts to generate realistic images through some black-box APIs. However, some studies reveal that image generators are notably susceptible to attacks and generate Not Suitable For Work (NSFW) contents by manually designed t

Zhiyao Tian, Shunhua Zhou, Anthony Lee, Yao Shan

Identifying earth pressures on in-service transportation tunnel linings is essential for their health monitoring and performance prediction, particularly in structures that exhibit poor performance. Due to the high costs associated with pressure gauges, pressure inversion based on easily observed structural responses, such as deformations, is preferred. A si

Yongzhi Huang, Fengjun Xi, Liyun Tu, Jinxin Zhu

Accurate segmentation of multiple organs in Computed Tomography (CT) images plays a vital role in computer-aided diagnosis systems. While various supervised learning approaches have been proposed recently, these methods heavily depend on a large amount of high-quality labeled data, which are expensive to obtain in practice. To address this challenge, we prop

Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang

Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). Fro

Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi, Francesc Lordan

The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation a

Vishwanath Pratap Singh, Md Sahidullah, Tomi Kinnunen

The accuracy of modern automatic speaker verification (ASV) systems, when trained exclusively on adult data, drops substantially when applied to children's speech. The scarcity of children's speech corpora hinders fine-tuning ASV systems for children's speech. Hence, there is a timely need to explore more effective ways of reusing adults' speech data. One pr

Juan M Gorriz, J. Ramirez, F. Segovia, F. J. Martinez-Murcia

Regression analysis is a central topic in statistical modeling, aimed at estimating the relationships between a dependent variable, commonly referred to as the response variable, and one or more independent variables, i.e., explanatory variables. Linear regression is by far the most popular method for performing this task in various fields of research, such

ATLAS Collaboration

A search for a new $Z'$ gauge boson predicted by $L_{\mu}-L_{\tau}$ models, based on charged-current Drell-Yan production, $pp \rightarrow W^{\pm(*)} \rightarrow Z' \mu^{\pm} \nu \rightarrow \mu^{\pm}\mu^{\mp}\mu^{\pm}\nu$, is presented. The data sample used corresponds to an integrated luminosity of 140 fb$^{-1}$ of proton-proton collisions at $\sqrt{s} = 1

Anne M. Green

Historically the most popular dark matter candidates have been new elementary particles, such as Weakly Interacting Massive Particles and axions. However Primordial Black Holes (PBHs), black holes formed from overdensities in the early Universe, are another possibility. The discovery of gravitational waves from mergers of tens of Solar mass black hole binari

J. L. Boldrini, B. Climent-Ezquerra, M. A. Rojas-Medar, M. D. Rojas-Medar

We prove results on existence and uniqueness of solutions of a system of equations modeling the evolution of a generalized bioconvective flow. The mathematical model considered in the present work describes the convective motion generated by the upward swimming of a culture of microorganisms under the influence of vertical gravitational forces, in an incompr

Andreas Schellenberger, Kai Phillip Schmidt

We investigate classes of interacting quantum spin systems in a single-mode cavity with a Dicke coupling, as a paradigmatic example of strongly correlated light-matter systems. Coming from the limit of weak light-matter couplings and large number of matter entities, we map the relevant low-energy sector of a broad class of models in the non-superradiant phas

Jonas Hansson, Emma Tegling

This paper presents a novel control design for vehicular formations, which is an alternative to the conventional second-order consensus protocol. The design is motivated by the closed-loop system, which we construct as first-order systems connected in series, and is therefore called serial consensus. The serial consensus design will guarantee stability of th

Blanca Climent-Ezquerra, Elva Ortega-Torres, Marco Rojas-Medar

We prove new regularity criteria of the Prodi-Serrin type with weak Lebesgue integrability in both space and time for a viscous active chemical fluid in a bounded domain.

Hamza Rami, Jhony H. Giraldo, Nicolas Winckler, Stéphane Lathuilière

Online Unsupervised Domain Adaptation (OUDA) for person Re-Identification (Re-ID) is the task of continuously adapting a model trained on a well-annotated source domain dataset to a target domain observed as a data stream. In OUDA, person Re-ID models face two main challenges: catastrophic forgetting and domain shift. In this work, we propose a new Source-gu

Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex

Samuel Kernan Freire, Margo MC van Mol, Carola Schol, Elif Özcan Vieira

Intensive care unit (ICU) patients often develop new health-related problems in their long-term recovery. Health care professionals keeping a diary of a patient's stay is a proven strategy to tackle this but faces several adoption barriers, such as lack of time and difficulty in knowing what to write. Large language models (LLMs), with their ability to gener

Mark Fortune, Neale P. Gibson, Daniel Foreman-Mackey, Thomas M. Evans-Soma

The use of Gaussian processes (GPs) is a common approach to account for correlated noise in exoplanet time series, particularly for transmission and emission spectroscopy. This analysis has typically been performed for each wavelength channel separately, with the retrieved uncertainties in the transmission spectrum assumed to be independent. However, the pre

Mengke Wang, Hao Yao, Jiayao Deng, Zhefeng Hu

Polarization splitter-rotators (PSRs) are the key elements to realize on-chip polarization manipulation. Current PSRs on thin film lithium niobate (TFLN) rely on sub-micron gaps to realize modes separation, which increase the difficulties of lithography and etching. In this paper, a polarization splitter-rotator on TFLN based on multimode interference (MMI)

Xin Yi, Linlin Wang, Xiaoling Wang, Liang He

Impressive results have been achieved in natural language processing (NLP) tasks through the training of large language models (LLMs). However, these models occasionally produce toxic content such as insults, threats, and profanity in response to certain prompts, thereby constraining their practical utility. To tackle this issue, various finetuning-based and

Ludovic Bonneau, Nathanael Kontowicz, Johann Bartel, Herve Molique

Low-lying bandhead states in axially prolate deformed odd-odd nuclei have long been described essentially within the rotor+two-quasiparticle picture. This approach allows one to explain the appearance of so-called Gallagher-Moszkowski doublets of bandheads with $K = \Omega_n \pm \Omega_p$, sum and difference of neutron and proton angular momentum projections

Xinglin Lyu, Junhui Li, Yanqing Zhao, Min Zhang

Generally, the decoder-only large language models (LLMs) are adapted to context-aware neural machine translation (NMT) in a concatenating way, where LLMs take the concatenation of the source sentence (i.e., intra-sentence context) and the inter-sentence context as the input, and then to generate the target tokens sequentially. This adaptation strategy, i.e.,

Nicolas Gaudy, Mathieu Salanne, Céline Merlet

Molecular dynamics simulations have been used extensively to determine equilibrium properties of the electrode-electrolyte interface in supercapacitors held at various potentials. While such studies are essential to understand and optimize the performance of such energy storage systems, investigations of the dynamics of adsorption during the charge of the su

Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning, Hai-Bo Ye

Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and unknown classes. Traditional methods prioritize selecting informative examples with low confidence, with the risk of mistakenly selecting unknown-class examples with similarly low co

Homayoun Honari, Mehran Ghafarian Tamizi, Homayoun Najjaran

Safe reinforcement learning (Safe RL) refers to a class of techniques that aim to prevent RL algorithms from violating constraints in the process of decision-making and exploration during trial and error. In this paper, a novel model-free Safe RL algorithm, formulated based on the multi-objective policy optimization framework is introduced where the policy i

Puhui Zhang, Jiacheng Zhang, Linpeng Gu, Liang Fang

We propose and demonstrate an on-chip 1*N power splitter based on topological photonic crystal (TPC) on a monolithic silicon photonic platform. Benefiting from the valley-locked propagation mode at the interface of TPCs with different topological phases, the proposed power splitter has negligible backscattering around the sharp bendings and good robustness t

Silvan Mertes, Dominik Schiller, Michael Dietz, Elisabeth André

In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges

Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali

Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins. While diffusion models are trained to represent the distribution in the training dataset, we often are more concerned with other properties, such as the aesthetic quality of the generated images or the functional properties of generated proteins. Dif

X. Wang, I. Krois, N. Ha-Van, M. S. Mirmoosa

Conventional wireless power transfer systems are linear and time-invariant, which sets fundamental limitations on their performance, including a tradeoff between transfer efficiency and the level of transferred power. In this paper, we introduce and study a possibility of temporal modulation for inductive wireless power transfer systems and uncover that this

Ana M. Montero, Andrés Santos

This study investigates the (longitudinal) thermodynamic and structural characteristics of single-file confined square-well and square-shoulder disks by employing a mapping technique that transforms the original system into a one-dimensional polydisperse mixture of nonadditive rods. Leveraging standard statistical-mechanical techniques, exact results are der

Vlad C. Andrei, Xinyang Li, Maresa Fees, Andreas Feik

In this paper, a digital twinning framework for indoor integrated sensing, communications, and robotics is proposed, designed, and implemented. Besides leveraging powerful robotics and ray-tracing technologies, the framework also enables integration with real-world sensors and reactive updates triggered by changes in the environment. The framework is designe

Chao Min, Liwei Wang

We consider the smallest eigenvalue distributions of some Freud unitary ensembles, that is, the probabilities that all the eigenvalues of the Hermitian matrices from the ensembles lie in the interval $(t,\infty)$. This problem is related to the Hankel determinants generated by the Freud weights with a jump discontinuity. By using Chen and Ismail's ladder ope

Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng

Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities. To address these challenges, we present BioELQA, a novel model that treats Biomedical Entity Linking as Multiple Choice Question Answering. BioELQA first obtains candidate entities with a

Sungwoo Park, Junyeop Kwon, Byeongnoh Kim, Suhyun Chae

This paper studies performative risk minimization, a formulation of stochastic optimization under decision-dependent distributions. We consider the general case where the performative risk can be non-convex, for which we develop efficient parameter-free optimistic optimization-based methods. Our algorithms significantly improve upon the existing Lipschitz ba

Di Wu, Haoyang Lan, Jiaxing Liu, Huangang Lu

Isomers, ubiquitous populations of relatively long-lived nuclear excited states, play a crucial role in nuclear physics. However, isomers with half-life times of several seconds or less barely had experimental cross section data due to the lack of a suitable measuring method. We report a method of online {\gamma} spectroscopy for ultra-short-lived isomers fr

Yifan Wu, Yousong Peng

In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the absence of effective methods, particularly in the domain of computational tools. This perspec

Guangyao Liu, Tianqi Mao, Ruiqi Liu, Zhenyu Xiao

Affine frequency division multiplexing (AFDM), tailored as a novel multicarrier technique utilizing chirp signals for high-mobility communications, exhibits marked advantages compared to traditional orthogonal frequency division multiplexing (OFDM). AFDM is based on the discrete affine Fourier transform (DAFT) with two modifiable parameters of the chirp sign

Justin Lien, Yan-Ning Kuo, Hiroyasu Ando, Shoichiro Kido

In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The Linear Inverse Model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. In this study, we introduce a new variant of LIM, called Colored-LIM,

Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang

Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural Networks (GNNs) have emerged as promising GSL solutions, utilizing recursive message passing to encode node-wise inter-dependencies. However, many existing GSL methods heavily depe

Julien Hofmann, Charles Thiébaut, Michel Riondet, Pierre Lhuissier

The proposed study investigates the damage mechanisms of martensitic stainless steel X3CrNiMo13-4 exposed to cavitation using two complementary experimental apparatus: ultrasonic horn (MUCEF) and hydrodynamic tunnel (PREVERO). Cavitation testing has been carried out on two different metallurgical states: QT780 and QT900 corresponding to coarse and fine micro

Joseph D. Clark, Xuenan Mi, Douglas A. Mitchell, Diwakar Shukla

Ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes often exhibit promiscuous substrate preferences that cannot be reduced to simple rules. Large language models are promising tools for predicting such peptide fitness landscapes. However, state-of-the-art protein language models are trained on relatively few peptide

Heegyu Kim, Sehyun Yuk, Hyunsouk Cho

Caution: This paper includes offensive words that could potentially cause unpleasantness. Language models (LMs) are vulnerable to exploitation for adversarial misuse. Training LMs for safety alignment is extensive and makes it hard to respond to fast-developing attacks immediately, such as jailbreaks. We propose self-refine with formatting that achieves outs

Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li

Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters. However, existing PEFT methods pose challenges in hyperparameter selection, such as choosing the rank for LoRA or Adapter, or specifying the length of soft promp

Sara Rastgoo, Foad Parsaei

In this article, a new family of asymptotically flat wormhole solutions in the context of symmetric teleparallel gravity, i.e., $f(Q)$ theory of gravity, are presented. Considering a power-law shape function and some different forms for $f(Q)$ function, we show that a wide variety of wormhole solutions for which the matter fields satisfy some energy conditio

Nicolas Jullien

Academic institutions and their staff use, adapt and create software. We're thinking of business tools used to carry out their mission: teaching management (Moodle) or subject teaching support (such as Maxima for formal calculus), for example. We're talking about software resulting from research work, designed by a researcher or a team as part of a research

Paz Hashash, Arkady Poliakovsky

In this paper, we provide a proof that functions belonging to Besov spaces $B^{r}_{q,\infty}(\mathbb{R}^N,\mathbb{R}^d)$, $q\in [1,\infty)$, $r\in(0,1)$, satisfy the following formula under a certain condition: \begin{equation} \label{eq:main result in abstract} \lim_{{\epsilon}\to 0^+}\frac{1}{|\ln{\epsilon}|}\left[u_{\epsilon}\right]^q_{W^{r,q}(\mathbb{R}^

D. Bajpai, A. Best, I. Ostrovskiy, D. Poitras

Coating detector materials with films highly reflective in the ultraviolet region improves sensitivity of the rare-event detectors that use liquid xenon. In this work, we investigate the MgF$_2$-Al-MgF$_2$ coating designed to achieve high reflectance at 175 nm, the mean wavelength of liquid xenon (LXe) scintillation. The coating was applied to an unpolished,

M. M. Khalilloev, B. O. Jabbarova, A. A. Nasirov

The dependence of random telegraph noise (RTN) amplitude on the gate overdrive in a junctionless field-effect transistor (FinFET) with rectangular and trapezoidal channel (fin) cross sections manufactured using silicon-on-insulator technology has been numerically simulated. It is established that the RTN amplitude in the subthreshold region of gate voltages

Yufei Huang, Shengding Hu, Xu Han, Zhiyuan Liu

Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models, which challenge human intuition and are crucial for a deeper understanding of neural models. In this paper, we present a comprehensive framework that provides a unified view of these three phenomena, focusing

Pablo Donato

We introduce the flower calculus, a deep inference proof system for intuitionistic first-order logic inspired by Peirce's existential graphs. It works as a rewriting system over inductive objects called ''flowers'', that enjoy both a graphical interpretation as topological diagrams, and a textual presentation as nested sequents akin to coherent formulas. Imp

Yanjun Zhao, Sizhe Dang, Haishan Ye, Guang Dai

Fine-tuning large language models (LLMs) with classic first-order optimizers entails prohibitive GPU memory due to the backpropagation process. Recent works have turned to zeroth-order optimizers for fine-tuning, which save substantial memory by using two forward passes. However, these optimizers are plagued by the heterogeneity of parameter curvatures acros

Leon Sick, Dominik Engel, Pedro Hermosilla, Timo Ropinski

Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks. While vanilla MAEs put equal emphasis on reconstructing the individual parts of the image, we propose to inform the reconstruction process through an attention-guided loss function. By leveraging advances in unsupervised object

Julien Zhou, Pierre Gaillard, Thibaud Rahier, Houssam Zenati

We address the problem of stochastic combinatorial semi-bandits, where a player selects among P actions from the power set of a set containing d base items. Adaptivity to the problem's structure is essential in order to obtain optimal regret upper bounds. As estimating the coefficients of a covariance matrix can be manageable in practice, leveraging them sho

Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang

With the incorporation of the UNet architecture, diffusion probabilistic models have become a dominant force in image generation tasks. One key design in UNet is the skip connections between the encoder and decoder blocks. Although skip connections have been shown to improve training stability and model performance, we reveal that such shortcuts can be a lim

Nika Haghtalab, Mingda Qiao, Kunhe Yang

Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by a high Price of Stability (PoS) in recent literature [Blum et al., 2021]. Implicit in the traditional PoS analysis is the assumption that agents have full knowledge of how their ta

Philippe Robutel, Adrien Leleu

Co-orbital exoplanets are a by-product of the models of formation of planetary systems. However, none have been detected in nature thus far. Although challenging, the observation of co-orbital exoplanets would provide valuable information on the formation of planetary systems as well as on the interactions between planets and their host star. After a brief r

Haotian Ye, Ruichen Li, Yuntian Gu, Yiping Lu

In quantum mechanics, the wave function of fermion systems such as many-body electron systems are anti-symmetric (AS) and continuous, and it is crucial yet challenging to find an ansatz to represent them. This paper addresses this challenge by presenting an ${\widetilde O}(N^2)$ ansatz based on permutation-equivariant functions. We prove that our ansatz can

Pengchao Han, Chao Huang, Geng Tian, Ming Tang

Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, where clients train one part in a parallel federated manner, and a main server trains the other. Despite the recent research on SFL algorithm development, the convergence analysis o

Lei Qiao, Jiangbin Gong

This work reports the spontaneous emergence of a photon current in a class of spin-cavity systems, where an assemble of quantum emitters interact with distinct photon modes confined in tunneling-coupled cavities. Specifically, with necessary symmetry breaking, photons in a superradiant phase afforded by coherent photon-emitter interaction spontaneously flow

Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang

Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we intro

Harshit Kumar, Beomseok Kang, Biswadeep Chakraborty, Saibal Mukhopadhyay

This paper presents the first systematic study of evaluating Deep Neural Networks (DNNs) designed to forecast the evolution of stochastic complex systems. We show that traditional evaluation methods like threshold-based classification metrics and error-based scoring rules assess a DNN's ability to replicate the observed ground truth but fail to measure the D

Jongyoon Song, Nohil Park, Bongkyu Hwang, Jaewoong Yun

Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input document. In this paper, we analyze the robustness of fine-tuning based summarization models to the knowledge conflict, which we call factual adaptiveness. We utilize pre-trained lang

Amneet Pal Singh Bhalla, Neelesh A. Patankar

Numerical simulation of moving immersed solid bodies in fluids is now practiced routinely following pioneering work of Peskin and co-workers on immersed boundary method (IBM), Glowinski and co-workers on fictitious domain method (FDM), and others on related methods. A variety of variants of IBM and FDM approaches have been published, most of which rely on us

Junmo Cho, Jaesik Yoon, Sungjin Ahn

Episodic memory plays a crucial role in various cognitive processes, such as the ability to mentally recall past events. While cognitive science emphasizes the significance of spatial context in the formation and retrieval of episodic memory, the current primary approach to implementing episodic memory in AI systems is through transformers that store tempora

Jin Yao, Eli Chien, Minxin Du, Xinyao Niu

This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, with a focus on pre-trained models--a notably under-researched area. Our research delineates a comprehensive framework for machine unlearning in pre-trained LLMs, encompassing a critical a

Víctor González-Alonso, Sara Torelli

Given a smooth projective complex curve inside a smooth projective surface, one can ask how its Hodge structure varies when the curve moves inside the surface. In this paper we develop a general theory to study the infinitesimal version of this question in the case of ample curves. We can then apply the machinery to show that the infinitesimal variation of H

S. J. Qian

The kinematics and flux evolution of the superluminal knots in blazar 3C345 were interpreted in the framework of the precessing jet-nozzle scenario with a precessing common helical trajectory-pattern. We show that the jet in 3C345 precesses with a period of 7.3yr and the superluminal knots move consistently along a precessing common helical trajectory-patter

Ashok B. Joshi

In this paper, we investigate the caustic point developed by outgoing null hypersurfaces and the behavior of null curves. We analyze three cases: Future, past, and zero caustic point terminology for outgoing null hypersurfaces in the marginally bound Lemaitre-Tolman-Bondi (LTB) metric for homogenous and inhomogeneous cases defined in the present work. We sho

Seonwoo Lee, Jihyun Mun, Sunhee Kim, Minhwa Chung

Despite the growing demand for digital therapeutics for children with Autism Spectrum Disorder (ASD), there is currently no speech corpus available for Korean children with ASD. This paper introduces a speech corpus specifically designed for Korean children with ASD, aiming to advance speech technologies such as pronunciation and severity evaluation. Speech

Hansheng Diao, Zijian Yao

We extend the construction of A$_{\rm inf}$-cohomology by Bhatt-Morrow-Scholze to the context of log $p$-adic formal schemes over a log perfectoid base. In particular, using coordinates, we prove comparison theorems between log A$_{\rm inf}$-cohomology with other $p$-adic cohomology theories, including log de Rham, log (q-)crystalline, log prismatic, and Kum

Junlong Liu, Xichen Shang, Huawen Feng, Junhao Zheng

Unsupervised sentence embeddings task aims to convert sentences to semantic vector representations. Most previous works directly use the sentence representations derived from pretrained language models. However, due to the token bias in pretrained language models, the models can not capture the fine-grained semantics in sentences, which leads to poor predict

Yihao Zhang, Hangzhou He, Jingyu Zhu, Huanran Chen

Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, yet suffers from inevitably decreased clean accuracy. Instead of perturbing the samples, Sharpness-Aware Minimization (SAM) perturbs the model weights during training to find a more

Jeong Hun Yeo, Seunghee Han, Minsu Kim, Yong Man Ro

In visual speech processing, context modeling capability is one of the most important requirements due to the ambiguous nature of lip movements. For example, homophenes, words that share identical lip movements but produce different sounds, can be distinguished by considering the context. In this paper, we propose a novel framework, namely Visual Speech Proc

Qifan Chen, Zheng Sun, Yulong Xing

In this paper, we present a novel class of high-order Runge--Kutta (RK) discontinuous Galerkin (DG) schemes for hyperbolic conservation laws. The new method extends beyond the traditional method of lines framework and utilizes stage-dependent polynomial spaces for the spatial discretization operators. To be more specific, two different DG operators, associat

Jiayu Joyce Chen, Steven E. Shladover

As driverless automated driving systems (ADS) start to operate on public roads, there is an urgent need to understand how safely these systems are managing real-world traffic conditions. With data from the California Public Utilities Commission (CPUC) becoming available for Transportation Network Companies (TNCs) operating in California with and without huma

Xing-Jian Lv, Xiao-Jun Bi, Kun Fang, Peng-Fei Yin

The excesses in the electron and positron spectra observed by many experiments, such as PAMELA and AMS-02, have sparked significant theoretical investigation. It is not easy to distinguish the two primary hypotheses dark matter annihilation/decay and pulsars from the spectral features. Should pulsars be the source of this excess, the expected variability in

Harishankar Manikantan

We develop a mean-field model to examine the stability of a `quasi-2D suspension' of elongated particles embedded within a viscous membrane. This geometry represents several biological and synthetic settings, and we reveal mechanisms by which the anisotropic mobility of particles interacts with long-ranged viscous membrane hydrodynamics. We first show that a

Mingqi Lv, HongZhe Gao, Xuebo Qiu, Tieming Chen

APT (Advanced Persistent Threat) with the characteristics of persistence, stealth, and diversity is one of the greatest threats against cyber-infrastructure. As a countermeasure, existing studies leverage provenance graphs to capture the complex relations between system entities in a host for effective APT detection. In addition to detecting single attack ev

Ryoya Yamasaki, Toshiyuki Tanaka

Blurring mean shift (BMS) algorithm, a variant of the mean shift algorithm, is a kernel-based iterative method for data clustering, where data points are clustered according to their convergent points via iterative blurring. In this paper, we analyze convergence properties of the BMS algorithm by leveraging its interpretation as an optimization procedure, wh

Xin Lyu, Hongxun Wu, Junzhao Yang

We study the cost of parallelizing weak-to-strong boosting algorithms for learning, following the recent work of Karbasi and Larsen. Our main results are two-fold: - First, we prove a tight lower bound, showing that even "slight" parallelization of boosting requires an exponential blow-up in the complexity of training. Specifically, let $\gamma$ be the weak

Ningqiang Song, Liangliang Su, Lei Wu

Neutron stars provide ideal astrophysical laboratories for probing new physics beyond the Standard Model. If axions exist, photons can develop linear polarization during photon-axion conversion in the magnetic field of a neutron star. We find that the plasma in the neutron star magnetosphere could dramatically enhance the polarization through the resonant co

Shota Sugawara, Ryuji Imamura

Developing an accurate and fast anomaly detection model is an important task in real-time computer vision applications. There has been much research to develop a single model that detects either structural or logical anomalies, which are inherently distinct. The majority of the existing approaches implicitly assume that the anomaly can be represented by iden

Zhaohui Fu, Jie Shen, Jiang Yang

In this paper, we develop a general framework for constructing higher-order, unconditionally energy-stable exponential time differencing Runge-Kutta methods applicable to a range of gradient flows. Specifically, we identify conditions sufficient for ETDRK schemes to maintain the original energy dissipation. Our analysis reveals that commonly used third-order

Pipi Hu

Perturbation and operator adjoint method are used to give the right adjoint form rigourously. From the derivation, we can have following results: 1) The loss gradient is not an ODE, it is an integral and we shows the reason; 2) The traditional adjoint form is not equivalent with the back propagation results. 3) The adjoint operator analysis shows that if and

Yonglin Jing

Hyper-relational knowledge graphs (KGs) contain additional key-value pairs, providing more information about the relations. In many scenarios, the same relation can have distinct key-value pairs, making the original triple fact more recognizable and specific. Prior studies on hyper-relational KGs have established a solid standard method for hyper-relational

N. Chems Eddine, D. D. Repovš

We consider a class of noncooperative Schr\"{o}dinger-Kirchhoff type system which involves a general variable exponent elliptic operator with critical growth. Under certain suitable conditions on the nonlinearities, we establish the existence of infinitely many solutions for the problem by using the limit index theory, a version of concentration-compactness

Siyao Liu, Yong Wang, Yuchen Yang

In [5] and [19], the authors gave anomaly cancellation formulas for the gauge groups E8,E8*E8. In this paper, we mainly deal with the case of gauge group E8*E8*E8. Using the E8*E8*E8 bundle, we construct some modular forms over SL2(Z). By these modular forms, we get some new anomaly cancellation formulas of characteristic forms.

Ilker Demirel, Edward De Brouwer, Zeshan Hussain, Michael Oberst

Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major limitation of existing procedures is not accounting for censoring, despite the abundance of RCTs and OSes that report r

Baptiste Calmès, Yonatan Harpaz, Denis Nardin

We establish fundamental motivic results about hermitian K-theory without assuming that 2 is invertible on the base scheme. In particular, we prove that both quadratic and symmetric Grothendieck-Witt theory satisfy Nisnevich descent, and that symmetric Grothendieck-Witt theory further satisfies a projective bundle formula, as well as d\'evissage and A^1-inva

Jaden Myers, Keyhan Najafian, Farhad Maleki, Katie Ovens

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for mod

Kun Yi, Qi Zhang, Hui He, Kaize Shi

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order

N. Chems Eddine, M. A. Ragusa, D. D. Repovš

We obtain critical embeddings and the concentration-compactness principle for the anisotropic variable exponent Sobolev spaces. As an application of these results,we confirm the existence of and find infinitely many nontrivial solutions for a class of nonlinear critical anisotropic elliptic equations involving variable exponents and two real parameters. With

Soma Sato, Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda

Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. This is also true for sentence embedding learning, where a decoder-based model, PromptEOL, has achieved the best performance on semantic textual similarity (STS) tasks. However, PromptEOL requires a manually annotated natural language inferenc

Guanming Xiong, Junwei Bao, Wen Zhao

This study explores the realm of knowledge base question answering (KBQA). KBQA is considered a challenging task, particularly in parsing intricate questions into executable logical forms. Traditional semantic parsing (SP)-based methods require extensive data annotations, which result in significant costs. Recently, the advent of few-shot in-context learning

Panpan Ren, Feng-Yu Wang, Simon Wittmann

To study diffusion processes on the p-Wasserstein space $\mathscr P_p$ for $p\in [1,\infty)$ over a separable, reflexive Banach space $X$, we present a criterion on the quasi-regularity of Dirichlet forms in $L^2(\mathscr P_p,\Lambda)$ for a reference probability $\Lambda$ on $\mathscr P_p$. It is formulated in terms of an upper bound condition with the unif

Noriaki Kawaguchi

We provide an alternative view of some results in [1, 3, 11]. In particular, we prove that (1) if a continuous self-map of a compact metric space has the shadowing, then the union of the basins of terminal chain components is a dense $G_\delta$-subset of the space; and (2) if a continuous self-map of a locally connected compact metric space has the shadowing